Bas van Stein
Assistant professor
- Name
- Dr. B. van Stein
- Telephone
- +31 71 527 7052
- b.van.stein@liacs.leidenuniv.nl
- ORCID iD
- 0000-0002-0013-7969
Bas van Stein is a researcher in the Natural Computing Group of LIACS and manager of the applied data science lab. He received his PhD in Computer Science from Leiden University in 2018. Bas’ research interest are in automated machine learning, global (Bayesian) optimization and neural architecture search.
Bas van Stein is a researcher in the Natural Computing Group of LIACS and manager of the applied data science lab. He received his PhD in Computer Science from Leiden University in 2018. Bas’ research interest are in automated machine learning, global (Bayesian) optimization and neural architecture search.
Next to his research, Bas has founded several software solution oriented companies such as Van Stein & Groentjes – Custom software development, and Smartnotation – the smart meeting minutes application.
Assistant professor
- Science
- Leiden Inst of Advanced Computer Science
- Winter R. de, Bronkhorst P., Stein B. van & Bäck T.H.W. (2022), Constrained multi-objective optimization with a limited budget of function evaluations, Memetic Computing 14: 151-174.
- Vermetten D.L., Caraffini F., Stein B. van & Kononova A.V. (2022), Using structural bias to analyse the behaviour of modular CMA-ES. In: Fieldsend J.E. (Ed.) GECCO '22: Proceedings of the genetic and evolutionary computation conference companion. Boston: ACM. 1674-1682.
- Winter R. de, Stein B. van & Bäck T.H.W. (2022), Multi-point acquisition function for constraint parallel efficient multi-objective optimization. In: Fieldsend E.J. (Ed.) GECCO '22: Proceedings of the genetic and evolutionary computation conference. New York: Association for Computing Machinery . 511-519.
- Kefalas M., Santiago Rojo J. de, Apostolidis A., Herik D. van den, Stein B. van & Bäck T.H.W. (2022), Explainable artificial intelligence for exhaust gas temperature of turbofan engines, Journal of Aerospace Information Systems 19(6): 447-454.
- Hanse G., Winter R. de, Stein B. van & Bäck T.H.W. (2022), Optimally weighted ensembles for efficient multi-objective optimization. In: Nicosia G., Ojha V., Malfa E. La, Malfa G. La, Jansen G., Pardalos P.M., Giuffrida G. & Umeton R. (Eds.) Machine Learning, Optimization, and Data Science. LOD 2021. no. 13163 Cham: Springer. 144-156.
- Saha S., de Jesus de Araujo Rios T., Minku L.L., Stein B. van, Wollstadt P., Yao X., Bäck T.H.W., Sendhoff B. & Menzel S. (2021), Exploiting generative models for performance predictions of 3D car designs: IEEE. 1-9.
- Vermetten D.L., Stein B. van, Kononova A.V. & Caraffini F. (2022), Analysis of structural bias in differential evolution configurations. In: Kumar B.V., Oliva D. & Suganthan P.N (Eds.) Differential evolution: from theory to practice. Studies in Computational Intelligence Singapore: Springer. 1-22.
- De Jesus de Araujo Rios T., Stein B. van, Bäck T.H.W., Sendhoff B. & Menzel S. (2022), Multi-task shape optimization using a 3D point cloud autoencoder as unified representation, IEEE Transactions on Evolutionary Computation 26(2): 206-217.
- Vermetten D.L., Stein B. van, Caraffini F., Minku L. & Kononova A.V. (2021), BIAS: a toolbox for benchmarking structural bias in the continuous domain: Institute of Electrical and Electronics Engineers (IEEE). [Working paper].
- Stein B. van, Caraffini F. & Kononova A.V. (2021), Emergence of structural bias in differential evolution. In: Chicano F. (Ed.) Proceedings of the Genetic and Evolutionary Computation Conference Companion.: ACM. 1234-1242.
- Winter R. de, Stein B. van & Bäck T.H.W. (2021), SAMO-COBRA: a fast surrogate assisted constrained multi-objective optimization algorithm. In: Ishibuchi, H.; Zhang, Q.; Cheng, R.; Li, K.; Li, H.; Wang, H.; Zhou, A. (Eds.) Evolutionary Multi-Criterion Optimization. no. 12654 Cham: Springer Nature Switzerland AG 2021. 270-282.
- deJesus de Araujo Rios T., Stein B. van Wollstadt P., Bäck T.H.W., Sendhoff B. & Menzel S. (2021), Exploiting local geometric features in vehicle design optimization with 3D point cloud autoencoders. In: 2021 IEEE Congress on Evolutionary Computation (CEC).: IEEE. 514-521.
- de Jesus de Araujo Rios T., Stein B. van, Bäck T.H.W., Sendhoff B. & Menzel S. (2021), Point2FFD: learning shape representations of simulation-ready 3D models for engineering design optimization. In: 2021 International Conference on 3D Vision (3DV).: IEEE. 1024-1033.
- Zeiser A.Z., Stein B van & Bäck T.H.W. (2021), Requirements towards optimizing analytics in industrial processes, Procedia Computer Science 184: 597-605.
- Winter R. de, Stein B. van & Bäck T.H.W. (2021), Ship design performance and cost optimization with machine learning. In: Bertram V. (Ed.) COMPIT'21. no. 20 Mülheim : Hamburg University of Technology. 185-196.
- Ponse K., Kononova A.V., Loleyt M. & Stein B. van (2021), Using machine learning to detect rotational and local reflectional symmetries in 2D images. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Orlando, Florida: IEEE. 01-08.
- Wang Y., Stein B. van, Bäck T.H.W. & Emmerich M.T.M. (2020), A Tailored NSGA-III for Multi-objective Flexible Job Shop Scheduling. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI).: IEEE. 2746-2753.
- Rios T., Kong J., Stein B. van, Bäck T.H.W., Wollstadt P., Sendhoff B. & Menzel S. (2020), Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI).: IEEE. 942-949.
- Stein B. van, Wang H. & Bäck T.H.W. (2020), Neural Network Design: Learning from Neural Architecture Search. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI).: IEEE. 1341-1349.
- Rios T., Stein B. van, Menzel S., Bäck T.H.W., Sendhoff B. & Wollstadt P. (2020), Feature visualization for 3d point cloud autoencoders. In: 2020 International Joint Conference on Neural Networks (IJCNN).: IEEE. 1-9.
- Wang Y., Stein B. van, Bäck T.H.W. & Emmerich M.T.M. (2020), Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local search. In: GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. New York: ACM. 181-182.
- Stein B. van, Wang H., Kowalczyk W.J., Emmerich M.T.M. & Bäck T.H.W. (2019), Cluster-based Kriging approximation algorithms for complexity reduction, Applied Intelligence .
- Winter R. de, Stein B. van, Dijkman, M., Bäck & T.H.W. (2019), Designing Ships Using Constrained Multi-objective Efficient Global Optimization. In: Nicosia G., Parda-los P., Giuffrida G., Umeton R., Sciacca V., (Eds.) Machine Learning, Optimization, and Data Science LOD2018. no. 11331 Cham: Springer International Publishing. 191–203.
- Guo X., Stein B. van & Bäck T.H.W. (2019), A New Approach Towards the Combined Algorithm Selection and Hyper-parameter Optimization Problem. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI).: IEEE. 2042-2049.
- Stein B. van, Wang H. & Bäck T.H.W. (2019), Automatic Configuration of Deep Neural Networks with Parallel Efficient Global Optimization. In: 2019 International Joint Conference on Neural Networks (IJCNN).: IEEE. 1-7.
- Rios T., Sendhoff B., Menzel S., Bäck T.H.W. & Stein B. van (2019), On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE). 791-798.
- Rios T., Wollstadt P., Stein B. van, Bäck T.H.W., Xu Z., Sendhoff B. & Menzel S. (2019), Scalability of Learning Tasks on 3D CAE Models Using Point Cloud Autoencoders. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE). 1367-1374.
- Stein B. van (20 September 2018), Data driven modeling & optimization of industrial processes (PhD thesis. Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University). Supervisor(s) and Co-supervisor(s): Bäck, T.H.W., Kowalczyk, W.J.
- Stein B. van, Wang H., Kowalczyk W.J. & Bäck T.H.W. (2018), A Novel Uncertainty Quantification Method for Efficient Global Optimization. In: Medina J., Ojeda-Aciego M., Verdegay J.L., Perfilieva I., Bouchon-Meunier B., Yager R.R. (Eds.) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 17th International Conference, IPMU 2018, Cádiz, Spain, June 11-15, 2018, Proceedings, Part III. no. Communications in Computer and Information Science, volume 855 Cham: Springer. 480-491.
- Stein B. van, Wang H. & Bäck T.H.W. (2018), Automatic Configuration of Deep Neural Networks with EGO.
- Rijn S.J. van, Wang H., Stein B. van & Bäck T.H.W. (2017), Algorithm Configuration Data Mining for CMA Evolution Strategies. In: GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference. New York: ACM. 737-744.
- Wang H., Stein B. van, Emmerich M.T.M. & Bäck T.H.W. (2017), Time complexity reduction in efficient global optimization using cluster kriging. In: GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference. New York: ACM. 889-896.
- Spek T. van der, Stein B. van, Holst M. van der & Bäck T.H.W. (2017), A Multi-Method Simulation of a High-Frequency Bus Line. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITCS).: IEEE Xplore. 1-6.
- Wang H., Stein B. van, Emmerich M.T.M. & Bäck T.H.W. (2017), A New Acquisition Function for Bayesian optimization based on the moment-generating function. In: Hao Wang, Stein B. van,Michael T. M.Emmerich, Bäck T.H.W. (Eds.) 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Banff, AB, Canada: IEEE. 507-512.
- Stein B. van, Leeuwen M. van & Bäck T. (2017), Local Subspace-Based Outlier Detection using Global Neighbourhoods. In: 2016 IEEE International Conference on Big Data (Big Data).: IEEE. 1136-1142.
- Stein B. van, Leeuwen M. van, Hao Wang, Purr S., Kreissl S., Meinhardt J. & Bäck T.H.W. (2017), Towards Data Driven Process Control in Manufacturing Car Body Parts. In: 2016 International Conference on Computational Science and Computational Intelligence CSCI.: IEEE CPS.
- Stein B. van, Hao Wang, Kowalczyk W.J., Michael T. M.Emmerich & Bäck T.H.W. (2016), Fuzzy clustering for Optimally Weighted Cluster Kriging. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).: IEEE. 978-1-5090-0627-4.
- Stein B. van & Kowalczyk W.J. (2016), An Incremental Algorithm for Repairing Training Sets with Missing Values. In: Carvalho J.P., Lesot M., Kaymak U., Vieira S. Bouchon-Meunier B., Yager R.R. (Eds.) Information Processing and Management of Uncertainty in Knowledge-Based Systems. no. 611 Switzerland: Springer International Publishing. 175-186.
- Stein B. van, Kowalczyk W.J. & Bäck T.H.W. (2016), Analysis and Visualization of Missing Value Patterns. In: Carvalho J., Lesot M.J., Kaymak U., Vieira S., Bouchon-Meunier B., Yager R. (Eds.) Information Processing and Management of Uncertainty in Knowledge-Based Systems. no. 611 Switzerland: Springer International Publishing. 187-198.
- Heiningen P. van, Stein B. van & Bäck T.H.W. (2016), A Framework for Evaluating Meta-models for Simulation-based Optimisation. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI).: IEEE.
- Stein B. van, Leeuwen M. van & Bäck T.H.W. (2016), Local Subspace-Based Outlier Detection using Global Neighbourhoods. In: 2016 IEEE International Conference on Big Data (Big Data).: IEEE Publishing.
- Stein B. van, Wang H., Kowalczyk W., Bäck T. & Emmerich M. (2015), Optimally Weighted Cluster Kriging for Big Data Regression. In: Fromont E., Bie T. de, Leeuwen M. van (Eds.) Advances in Intelligent Data Analysis XIV. no. LNCS 9385 Cham: Springer International Publishing. 310-321.
- Stein B. van, Michael T. M.Emmerich & Yang Z. (2013), Fitness Landscape Analysis of NK Landscapes and Vehicle Routing Problems by Expanded Barrier Trees. In: EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV.: Springer International Publishing. 75-89.
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